Case-control studies are widely used in epidemiology and biomedical research because they provide substantial efficiency gains when outcomes are rare or prospective follow-up is impractical. However, retrospective outcome-dependent sampling distorts the population outcome distribution, creating fundamental challenges for causal inference. We propose a unified three-stage weighting (3S-weighting) framework for causal inference and causal mediation analysis from case--control studies. The proposed approach first estimates the unknown population outcome prevalence using density-ratio learning and label-shift correction combined with externally available covariate information. Next, prevalence-based design weights are used to reconstruct the target population distribution from the retrospective sample. Finally, stabilized causal and mediation weights are applied within a marginal structural modeling framework to estimate total and pathway-specific causal effects, including the pure direct effect, pure indirect effect, and interaction effect. Simulation studies demonstrate that conventional analyses that ignore retrospective sampling can produce substantial bias in both total and mediation effect estimates, whereas the proposed approach consistently recovers the target population causal parameters across a range of sampling scenarios. An application of data from the National Health and Nutrition Examination Survey further illustrates the practical implementation and utility of the proposed framework.

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